Current Models to Address Obstacles to HCV Elimination
Bibliographic record
Abstract
To help inspire global action, the World Health Organization (WHO) has set an ambitious goal of eliminating viral hepatitis, including hepatitis C virus (HCV) infection, as a public health concern by 2030. Globally, an estimated 58 million people have chronic HCV infection, including over 4.5 million people who have recently injected drugs (PWID). Of the 1.5 million new infections occurring per year, over 43% are in this risk group. Systematic approaches are needed with this population to achieve the WHO elimination goals. A number of programs have been successful, most notably in Australia, Scotland, Iceland and North America. We still require additional programs that are easily accessible, multidisciplinary, durable and driven by patient-defined parameters of engagement. We have evaluated housing-based programs as community pop-up clinics to identify HCV-infected vulnerable inner-city residents and offer HCV treatment within such a context. This has been successful, with almost 300 individuals receiving treatment since January 2021, with an effective cure rate exceeding 98%, 99% retention in care, HCV reinfection rates below 1/100 person-years and reduced rates of opioid-related overdose deaths. The implementation of programs, such as ours, must be considered to achieve elimination of HCV infection among PWID on a worldwide basis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".